CLASSIFICATION OF ELECTROENCEPHALOGRAPHY SIGNAL USING STATISTICAL FEATURES AND REGRESSION CLASSIFIER

Transcription

1 CLASSIFICATION OF ELECTROENCEPHALOGRAPHY SIGNAL USING STATISTICAL FEATURES AND REGRESSION CLASSIFIER NURBAITY BINTI SABRI UNIVERSITI TEKNOLOGI MALAYSIA

2 CLASSIFICATION OF ELECTROENCEPHALOGRAPHY SIGNAL USING STATISTICAL FEATURES AND REGRESSION CLASSIFIER NURBAITY BINTI SABRI A dissertation submitted in partial fulfilment of the requirements for the award of the degree of Master of Science (Computer Science) Faculty of Computing Universiti Teknologi Malaysia JULY 2014

3 iii With love and gratitude to Allah, my beloved husband and family for their support, patience and being till the moment of my dissertation completion

4 iv ACKNOWLEDGEMENT In the name of Allah, the Most Gracious and Most Merciful First and foremost I would like to express my sincere gratitude to my project supervisor Prof. Dr. Dzulkifli Mohamad for his great support, nice motivation, proper guidance and constant encouragement which made this project enrich and insightful experience. His timely inputs, valuable return and constructive criticism in many stages of this work was act as catalyst in understanding and execution of this project. My lovely husband and family, thanks for your perpetual encouragement and support. To all my beloved friends and students of Master of Computer Science and my PhDs senior, thank you for all your support, endless help and encouragement. I would also like to thank the examiner and for their generous help in various ways for the completion of this thesis

5 v ABSTRACT Enormous digital electroencephalography (EEG) acquisition systems available nowadays for researchers due to the high demand in the brain signal research. Using EEG-based emotion recognition, the computer can look inside a user head to observe their mental state of sad and happy emotion. Thus, there is a need for efficient mechanism to detect those emotions accurately along with computation complexity. The current algorithms available are excessively complex with higher computational time. In this study, 14 channels of EEG signals acquired from emotive device with 128 Hz sample rate. These raw signals undergo preprocess stage using band pass and ICA filter. This research focuses two components which is feature extraction and classification. A combination of statistical features has been carrying out to extract important signal. To classify the EEG signal into sad and happy classes, Support Vector Machine (SVM) and Linear Regression has been applied. Waikato Environment for Knowledge Analysis (WEKA) as training tools is employ to train the dataset and test the accuracy of the classifier. Results presented that Linear Regression has better detection accuracy with 95% compared to SVM with 80% average accuracy. In conclusion this research suggests using Linear Regression for future work on predicting between sad and happy emotion from the EEG signal.

6 vi ABSTRAK Pelbagai alat pengesanisyarat elektroensefalografi Electroencephalography (EEG) berada dipasaran pada masa kini khusus untuk penyelidikan otak. Perkara ini berlaku disebabkan oleh permintaan yang tinggi didalam bidang kajian isyarat otak. Menggunakan EEG sebagai asas dalam kajian untuk mengenal pasti emosi, komputer digunakan untuk melihat kondisi otak dan mengenal pasti keadaan mental mereka. Justeru itu, mekanisma untuk mengenal pasti emosi secara tepat berserta dengan pengiraan yang ringkas dan mudah amat diperlukan. Algorithm yang ada sangat rumit dan tempoh pengiraan yang agak lama. Didalam kajian ini, 14 saluran isyarat EEG diperoleh daripada peralatan Emotiv beserta dengan 128Hz sample rate. Kesemua isyarat ini melalui fasa proses permulaan menggunakan penapis lulus jalur band pass dan penapis ICA. Kajian ini fokus kepada dua komponen iaitu pengekstrakan ciri-ciri dan pengkelasan. Kombinasi ciri-ciri statistik telah digunakan untuk mengekstrak isyarat yang penting. Alogritma Support Vector Machine (SVM) dan regresi linear telah digunakan telah digunakan untuk pengkelasan diantara emosi sedih dan gembira. Waikato Environment for Knowledge Analysis (WEKA) sebagai peralatan latihan juga telah diadaptasi untuk melatih data dan menguji ketepatan pengkelasan emosi. Keputusan menunjukkan bahawa regresi linear menghasilkan ketepatan yang lebih tinggi iaitu 95%, lebih tinggi daripada SVM iaitu hanya 80% purata ketepatan. Kesimpulan daripada kajian ini merumuskan bahawa penggunaan regresi linear untuk kerja-kerja berkaitan pengkelasan diantara emosi sedih dan gembira daripada isyarat EEG pada masa hadapan.